python_code stringlengths 0 1.02M | repo_name stringlengths 9 48 | file_path stringlengths 5 114 |
|---|---|---|
bolt-master | experiments/python/datasets/__init__.py | |
#!/usr/bin/env/python
import os
import numpy as np
from joblib import Memory
import pandas as pd
from . import paths
_memory = Memory('.', verbose=1, compress=9)
UCR_DATASETS_DIR = paths.UCR
UCR_INFO_PATH = paths.UCR_INFO
# ================================================================
# Public
# =============... | bolt-master | experiments/python/datasets/ucr.py |
#!/bin/env python
from __future__ import absolute_import, division, print_function
from scipy import io
import numpy as np
import os
from joblib import Memory
_memory = Memory('.', verbose=1)
DATADIR = '../datasets/svhn'
TRAIN_PATH = os.path.join(DATADIR, 'train_32x32.mat')
TEST_PATH = os.path.join(DATADIR, 'test_... | bolt-master | experiments/python/datasets/svhn.py |
#!/bin/env/python
"""utility functions for data munging"""
from __future__ import absolute_import, division, print_function
import numpy as np
import sklearn
def split_train_test(X, Y, train_frac=.8, random_state=123):
"""Returns X_train, X_test, y_train, y_test"""
np.random.seed(123)
return sklearn.mo... | bolt-master | experiments/python/datasets/data_utils.py |
#!/bin/env python
# Load 3-lead ECG recordings from SHAREE Database:
# https://physionet.org/content/shareedb/1.0.0/
from __future__ import division, print_function
import matplotlib.pyplot as plt
import numpy as np
import os
from . import paths
from . import files
from joblib import Memory
_memory = Memory('.', v... | bolt-master | experiments/python/datasets/sharee.py |
#!/bin/env python
# Load 3-lead ECG recordings from SHAREE Database:
# https://physionet.org/content/shareedb/1.0.0/
from __future__ import division, print_function
import matplotlib.pyplot as plt
import numpy as np
import os
from . import paths
from . import files
from joblib import Memory
_memory = Memory('.', v... | bolt-master | experiments/python/datasets/incart.py |
#!/bin/env python
# from __future__ import absolute_import, division, print_function
from __future__ import division, print_function
import numpy as np
from . import paths
from . import image_utils as imgs
from joblib import Memory
_memory = Memory('.', verbose=1)
DATADIR_101 = paths.CALTECH_101
DATADIR_256 = pat... | bolt-master | experiments/python/datasets/caltech.py |
#!/bin/env python
from __future__ import absolute_import, division, print_function
import numpy as np
from python import image_utils as imgs
from joblib import Memory
_memory = Memory('.', verbose=1)
DATADIR_101 = '../datasets/caltech/101_ObjectCategories'
def main():
import matplotlib.pyplot as plt
# c... | bolt-master | experiments/python/datasets/flowers.py |
#!/usr/bin/env python
from __future__ import print_function
import numpy as np
from sklearn.datasets import load_digits
import timeit
import bolt
# ================================================================ utils
def _dists_sq(X, q):
diffs = X - q
return np.sum(diffs * diffs, axis=-1)
def _dists_l... | bolt-master | tests/test_encoder.py |
# This file is part of Eigen, a lightweight C++ template library
# for linear algebra.
#
# Copyright (C) 2012 Keir Mierle <mierle@gmail.com>
#
# This Source Code Form is subject to the terms of the Mozilla
# Public License v. 2.0. If a copy of the MPL was not distributed
# with this file, You can obtain one at http://m... | bolt-master | cpp/src/external/eigen/scripts/relicense.py |
# Intentionally empty
| bolt-master | cpp/src/external/eigen/debug/gdb/__init__.py |
# -*- coding: utf-8 -*-
# This file is part of Eigen, a lightweight C++ template library
# for linear algebra.
#
# Copyright (C) 2009 Benjamin Schindler <bschindler@inf.ethz.ch>
#
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
... | bolt-master | cpp/src/external/eigen/debug/gdb/printers.py |
from setuptools import setup, find_packages
setup(
name = 'attention-tensorflow-mesh',
packages = find_packages(),
version = '0.0.2',
license='MIT',
description = 'A bunch of attention related functions, for constructing transformers in tensorflow mesh',
author = 'Phil Wang',
author_email = 'lucidrains@g... | attention-tensorflow-mesh-master | setup.py |
from attention_tensorflow_mesh.attention_tensorflow_mesh import transformer_lm, transformer, attention | attention-tensorflow-mesh-master | attention_tensorflow_mesh/__init__.py |
import math
import mesh_tensorflow as mtf
import tensorflow.compat.v1 as tf
# helpers
def default(val, d):
return val if val is not None else d
# simple linear layer
def linear(x, dim_out, scope = 'linear', bias = True):
with tf.variable_scope(scope):
*_, dim_in = x.shape
w_init_stdev = 1 / ... | attention-tensorflow-mesh-master | attention_tensorflow_mesh/attention_tensorflow_mesh.py |
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